1. Field of the Invention
This invention pertains generally to image coding, and more particularly to image coding using directional transforms and forms of entropy encoding.
2. Description of Related Art
Visual communication remains an important focus of high technology development. To communicate visual information with ever-increasing resolution (definition) over limited bandwidth resources requires finding improved image compression techniques. Increasingly efficient representations of the image data are sought, whereby significant information about the object of interest can be captured within the smallest amount of data possible.
Structured transforms (e.g., block based transforms) and wavelets have been used toward increasing compression efficiency. It will be appreciated that many codecs utilize vector quantization. International standards, including video compression MPEG-1, MPEG-2, MPEG-4, H.261 and H.263 all use a combination of the block Discrete Cosine Transform (DCT) and motion estimation/compensation. In addition, image coding standards, including JPEG and JPEG2000 utilize block-based DCT (JPEG), or wavelet and zero-trees (JPEG2000). Certain newer codecs utilize wavelet transform based image compression in response to a Discrete Wavelet Transform (DWT). However, wavelets provide limited directional ability for capturing image edges.
One of the more important forms of encoding is that of embedded encoding. Embedded coding may be defined in regard to two files being produced by an embedded encoder having files sizes of M and N bits, with M>N, whereby the file with size N is identical to the first N bits of the file with size M. The above is a limited definition, as embedded encoding generally allows a single file to be encoded for users having different quality needs, and in which only the necessary bits are either communicated and/or utilized according to the needed quality level.
FIG. 1 illustrates encoding flow for Embedded Zero-Tree Wavelet (EZW) encoding. An original image is transformed and coded according to embedded zero-tree wavelet (EZW) encoding which generates a dominant list and subordinate list, to which arithmetic coding is applied to output a coded bit stream. In performing embedded encoding, the coding is performed bit-plane by bit-plane, from the most significant bits down to the least significant. Typically, significant coefficients are replaced with zeros to mark them as being already-coded. Two types of output sequence lists are generated using EZW encoding: (1) dominant and (2) subordinate. The dominant list codes the significant map or location of significant coefficients into multiple symbols. By way of example and not limitation, a sequence of four symbols is output (1) “N”—negative significant coefficients; (2) “P”—positive significant coefficients; (3) “Z”—isolated zeros; and (4) “T”—zero-trees. In subordinate lists the magnitude of significant coefficients is encoded as an output sequence of 0s and 1s. In similar manner, encoding has also been performed using Set Partitioning in Hierarchical Trees (SPIHT), which generally provides improved performance over the EZW approach.
Often so called “separable” transforms have been utilized for realizing multiresolution image (embedded) representations. In these approaches 1D filters are used separately. Contrasted to the above are “nonseparable” transforms which utilize 2D filters and 2D downsampling matrices that cannot be factorized into 1D filters and downsampling pairs. The traditional wavelet transform (WT) is categorized as a separable transform, yet it provides limited diagonal selectivity as frequencies which represent different orientation are gathered into one subband in each resolution and are often truncated leading to image blur in diagonal orientations. Embedded encoding is successful because of the tree structure provided by the wavelet transform, which is applied in a separable manner for image coding.
Embedded Zerotree Wavelet (EZW) coding and Set Partitioning in Hierarchical Trees (SPIHT), have attempted to provide computationally simple techniques for image compression.
Contourlet transforms extend beyond 1D wavelets, into true 2D transforms that can capture intrinsic geometric structure. In this approach discrete domain multiresolution and multidirectional expansion is achieved utilizing contour segments and non-separable filter banks.
However, even these advanced techniques have shortcomings when encoding certain image forms, such as those containing directional energy.
Accordingly, a need exists for a system and method of enhancing image coding efficiency within embedded encoders. These needs and others are met within the present invention, which overcomes the deficiencies of previously developed embedded encoding apparatus and methods.